Instructions to use MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit
- SGLang
How to use MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit with Docker Model Runner:
docker model run hf.co/MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit
Update README.md
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README.md
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This is a 3bit AutoRound GPTQ version of Mistral-Large-Instruct-2407.
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This conversion used model-*.safetensors.
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This quantized model needs at least ~50GB + context (~5GB) VRAM. I quantized it so that it could fit 64GB VRAM.
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Quantization script (it takes around 520 GB RAM and A40 GPU 48GB around 20 hours to convert):
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```
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This is a 3bit AutoRound GPTQ version of Mistral-Large-Instruct-2407.
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This conversion used model-*.safetensors.
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+
This quantized model needs at least ~ 50GB + context (~5GB) VRAM. I quantized it so that it could fit 64GB VRAM.
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Quantization script (it takes around 520 GB RAM and A40 GPU 48GB around 20 hours to convert):
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```
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